Application of Grid-Search Based Dropout-LSTM model in the Prediction of COVID-19
To achieve more accurate analysis and prediction of the epidemic situation of COVID-19 2019(COVID-19),an LSTM(long-and short-term memory)based prediction model of COVID-19 is established,and the grid search method is used to opti-mize the three most critical super parameters.At the same time,in order to improve the prediction accuracy and solve the over-fitting phenomenon,the Dropout regularization is introduced to optimize the network.Testing shows that the LSTM model with multiple hidden layers has higher prediction accuracy than the traditional LSTM model.When the dropout ratio is 0.22,it can effectively solve the problem of over-fitting in model prediction.Compared with RNN(Recurrent Neural Network)model and SEIR(susceptible exposed infected recovered)model,the Dropout-LSTM model established in this experiment based on grid-search has the smallest root mean square error,average absolute error and average absolute percentage error.Therefore,the Dropout-LSTM model based on grid-search established in this experiment has better prediction ability.